{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_gen= ImageDataGenerator(width_shift_range=0.1,height_shift_range=0.1,shear_range=0.2,zoom_range=0.2,fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 14002 images belonging to 2 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.preprocessing.image.DirectoryIterator at 0x2728625f988>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory('C:\\\\Users\\\\Isna Khan\\\\project isna final AI\\\\pictures\\\\train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Activation,Dense,Dropout,Flatten,Conv2D,MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=(128,128,3),activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(filters=64,kernel_size=(3,3),input_shape=(128,128,3),activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(filters=64,kernel_size=(3,3),input_shape=(128,128,3),activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_7 (Conv2D)            (None, 126, 126, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 63, 63, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 61, 61, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 30, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 28, 28, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 12544)             0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 128)               1605760   \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 129       \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 1,662,209\n",
      "Trainable params: 1,662,209\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 14002 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_image_gen = image_gen.flow_from_directory('C:\\\\Users\\\\Isna Khan\\\\project isna final AI\\\\pictures\\\\train',\n",
    "                             target_size=(128,128),\n",
    "                             batch_size=16,\n",
    "                             class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5136 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_image_gen = image_gen.flow_from_directory('C:\\\\Users\\\\Isna Khan\\\\project isna final AI\\\\pictures\\\\test',\n",
    "                             target_size=(128,128),\n",
    "                             batch_size=16,\n",
    "                             class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'empty pallet': 0, 'occupied pallet': 1}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_gen.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "100/100 [==============================] - 79s 790ms/step - loss: 0.6183 - accuracy: 0.6500 - val_loss: 0.5918 - val_accuracy: 0.6771\n"
     ]
    }
   ],
   "source": [
    "results = model.fit_generator(train_image_gen,epochs=1,steps_per_epoch=100,\n",
    "                              validation_data =test_image_gen,validation_steps=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.65]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.history['accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27294445d08>]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(results.history['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('empty_occupied.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model = load_model('empty_occupied.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "pallet_file = Image.open('C:\\\\Users\\\\Isna Khan\\\\project isna final AI\\\\pictures\\\\test\\\\occupied\\\\occupied (37).jpg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27296202d08>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(pallet_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to wss://anvil.works/uplink\n",
      "Anvil websocket open\n",
      "Reconnecting Anvil Uplink...\n",
      "Authenticated OK\n"
     ]
    }
   ],
   "source": [
    "import anvil.server\n",
    "anvil.server.connect(\"63HRRXNPYJC3H6UEHR4PW6I2-MFIFBLU4YPOCQ35B\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import anvil.media\n",
    "\n",
    "@anvil.server.callable\n",
    "def classify_image(pallet_img):\n",
    "    with anvil.media.TempFile(pallet_img) as filename:\n",
    "        img = image.load_img(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import PIL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import anvil.media\n",
    "\n",
    "@anvil.server.callable\n",
    "def classify_image(img):\n",
    "    with anvil.media.TempFile(img) as filename:\n",
    "                                               img = image.load_img(img)\n",
    "        \n",
    "                                               img = img.resize((128, 128,3), resample= PIL.Image.BICUBIC)\n",
    "                                               arr = img_to_array(img)\n",
    "                                               arr = np.expand_dims(arr, axis=0)\n",
    "                                               arr /= 255.0\n",
    "                                               score = model.predict(arr)\n",
    "    return ('occupied' if score < 0.5 else 'empty', float(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
